925 resultados para Classification of functioning


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Hospitals attached to the Spanish Ministry of Health are currently using the International Classification of Diseases 9 Clinical Modification (ICD9-CM) to classify health discharge records. Nowadays, this work is manually done by experts. This paper tackles the automatic classification of real Discharge Records in Spanish following the ICD9-CM standard. The challenge is that the Discharge Records are written in spontaneous language. We explore several machine learning techniques to deal with the classification problem. Random Forest resulted in the most competitive one, achieving an F-measure of 0.876.

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A new classification of microtidal sand and gravel beaches with very different morphologies is presented below. In 557 studied transects, 14 variables were used. Among the variables to be emphasized is the depth of the Posidonia oceanica. The classification was performed for 9 types of beaches: Type 1: Sand and gravel beaches, Type 2: Sand and gravel separated beaches, Type 3: Gravel and sand beaches, Type 4: Gravel and sand separated beaches, Type 5: Pure gravel beaches, Type 6: Open sand beaches, Type 7: Supported sand beaches, Type 8: Bisupported sand beaches and Type 9: Enclosed beaches. For the classification, several tools were used: discriminant analysis, neural networks and Support Vector Machines (SVM), the results were then compared. As there is no theory for deciding which is the most convenient neural network architecture to deal with a particular data set, an experimental study was performed with different numbers of neuron in the hidden layer. Finally, an architecture with 30 neurons was chosen. Different kernels were employed for SVM (Linear, Polynomial, Radial basis function and Sigmoid). The results obtained for the discriminant analysis were not as good as those obtained for the other two methods (ANN and SVM) which showed similar success.

Proposal for a Council Regulation (EEC) on the common organization of the market in wine; Proposal for a Council Regulation (EEC) laying down special provisions relating to quality wines produced in specified regions; Proposal for a Council Regulation (EEC) laying down general rules for fixing the reference price and levying the countervailing charge for wine; Proposal for a Council Regulation (EEC) defining certain products falling within headings Nos 20.07, 22.04 and 22.05 of the Common Customs Tariff and originating in non-member countries; Proposal for a Council Regulation (EEC) on general rules for the classification of vine varieties; Proposal for a Council Regulation (EEC) concerning the addition of alcohol to products in the wine sector; Proposal for a Council Regulation (EEC) laying down general rules for the description and presentation of wines and grape musts; Proposal for a Council Regulation (EEC) on sparkling wines produced in the Community and defined in item 13 of Annex II to Regulation (EEC) No --- ; Proposal for a Council Regulation (EEC) on measures designed to adjust wine-growing potential to market requirements; Proposal for a Council Regulation (EEC) on the granting of a conversion premium in the wine sector; Proposal for a Council Regulation (EEC) laying down general rules for the import of wines, grape juice and grape must; Proposal for a Council Regulation (EEC) laying down general rules governing the distillation of wines provided for in Articles 11,12, 39 and 40 of Regulation (EEC) (submitted to the Council by the Commission). COM (78) 387 final, 2 October 1979

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The Maser thesis is devoted to developing a model to technical state of gas turbine engine estimation. The approaches to preparation data, especially to handle unbalanced data were presented in the thesis. In order to efficient estimation of model performance, the special metric was chosen. Goal of the master thesis is analyzing of monitoring parameters data and developing a model of technical state of GTE estimation based on the data.

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Questions of handling unbalanced data considered in this article. As models for classification, PNN and MLP are used. Problem of estimation of model performance in case of unbalanced training set is solved. Several methods (clustering approach and boosting approach) considered as useful to deal with the problem of input data.